Artificial Intelligence in Echocardiography for Anesthesiologists

• Published in: Journal of cardiothoracic and vascular anesthesia Vol. 35; no. 1; pp. 251 - 261
• Main Authors: Chen, Xia, Owen, Cindy A., Huang, Emma C., Maggard, Brittany D., Latif, Rana K., Clifford, Sean P., Li, Jinbao, Huang, Jiapeng
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2021
• Subjects: Anesthesiologists; anesthesiology; Artificial Intelligence; Cardiovascular Diseases; deep learning; Echocardiography; Humans; machine learning; Monitoring, Intraoperative; deep learning; anesthesiology; machine learning; artificial intelligence; echocardiography
• ISSN: 1053-0770; 1532-8422
• DOI: 10.1053/j.jvca.2020.08.048

Echocardiography is a unique diagnostic tool for intraoperative monitoring and assessment of patients with cardiovascular diseases. However, there are high levels of interoperator variations in echocardiography interpretations that could lead to inaccurate diagnosis and incorrect treatment. Furthermore, anesthesiologists are faced with the additional challenge to interpret echocardiography and make decisions in a limited timeframe from these complex data. The need for an automated, less operator-dependent process that enhances speed and accuracy of echocardiography analysis is crucial for anesthesiologists. Artificial intelligence is playing an increasingly important role in the medical field and could help anesthesiologists analyze complex echocardiographic data while adding increased accuracy and consistency to interpretation. This review aims to summarize practical use of artificial intelligence in echocardiography and discusses potential limitations and challenges in the future for anesthesiologists.